Hyperspectral image analysis using multifractal attributes

Sebastien Combrexelle, Herwig Wendt, Jean-Yves Tourneret, Stephen McLaughlin, Patrice Abry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
61 Downloads (Pure)


The increasing spatial resolution of hyperspectral remote sensors requires the development of new processing methods capable of combining both spectral and spatial information. In this article, we focus on the spatial component and propose the use of novel multifractal attributes, which extract spatial information in terms of the fluctuations of the local regularity of image amplitudes. The novelty of the proposed approach is twofold. First, unlike previous attempts, we study attributes that efficiently summarize multifractal information in a few parameters. Second, we make use of the most recent developments in multifractal analysis: Wavelet leader multifractal formalism, the current theoretical and practical benchmark in multifractal analysis, and a novel Bayesian estimation procedure for one of the central multifractal parameters. Attributes provided by these state-of-the-art multifractal analysis procedures are studied on two sets of hyperspectral images. The experiments suggest that multifractal analysis can provide relevant spatial/textural attributes which can in turn be employed in tasks such as classification or segmentation.

Original languageEnglish
Title of host publication2015 7th Workshop on Hyperspectral Image and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing (WHISPERS)
ISBN (Electronic)9781467390156
Publication statusPublished - 23 Oct 2017
Event7th Workshop on Hyperspectral Image and Signal Processing - Tokyo, Japan
Duration: 2 Jun 20155 Jun 2015


Conference7th Workshop on Hyperspectral Image and Signal Processing


  • Hyperspectral imaging
  • multifractal analysis
  • spatial information
  • texture characterization
  • wavelet leaders

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing


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